# Towards Generating a Multimodal and Multivariate Classification Model from Imaging and Non-Imaging Measures for Accurate Diagnosis and Monitoring of Dementia in Parkinsons disease

> **NIH NIH R01** · UNIVERSITY OF ALABAMA AT BIRMINGHAM · 2024 · $681,430

## Abstract

PROJECT SUMMARY
The goal of the proposed research is to identify the best predictive biomarkers of dementia in Parkinson’s disease
(PDD) through a multimodal and multivariate statistical model utilizing both neuroimaging derived measures
(diffusion-weighted MRI (dMRI), resting-state functional MRI (rsfMRI), and T1-weighted MRI measures) and non-
imaging measures such as demographics (age, sex, years of education), clinical (disease duration and severity),
genetics (LRRK2), and CSF-measures (Total Tau, β-Amyloid, α-synuclein). It is critical to identify biomarkers
that can predict dementia in Parkinson’s disease (PD) as approximately 50-80% of PD patients develop PDD
within twelve years of diagnosis. Identifying pathophysiology-based biomarkers that could identify PD patients
at high risk for PDD reliably is critical for better prognostication, correct identification of PDD in its prodromal
stage to recruit in new disease-modifying clinical trials, and better understanding the pathophysiological
processes underlining PDD. The proposed project has two important components. The first component of the
project is to understand the pathophysiological mechanism underlying PDD through sophisticated voxelwise
dMRI-derived measures estimated using a multi-shell high angular and spatial resolution dMRI data acquisition,
and understanding network-level white matter (WM)-derived structural connectivity and rsfMRI-derived functional
connectivity in PDD. The second component of the project is to identify the biomarkers that predict PDD through
multivariate statistical modelling by combining these sophisticated pathologically relevant neuroimaging
measures with non-imaging measures (such as clinical, demographics, genetics, and CSF-measures). We will
recruit demographically matched healthy controls (HC) along with demographically, disease duration, and
disease severity matched PD patients with mild cognitive impairment (PD-MCI), PD-non-MCI (PD-nMCI), and
PDD for this project. We will acquire multi-shell dMRI data at three b-values, namely 500s/mm2, 1000s/mm2, and
2500s/mm2 with a high angular and spatial resolution and estimate various unbiased free-water (fiso) corrected
Gaussian dMRI-derived measures along with non-Gaussian dMRI-derived measures such as diffusion kurtosis
measures, and neurite orientation dispersion and density imaging measures. We will further compare these
measures between the groups to identify significant dMRI-derived measures separating the groups, and
understanding the neuroanatomical correlates of these measures with various neuropsychological scores.
Furthermore, we will estimate dMRI-derived structural connectivity and rsfMRI-derived functional connectivity to
understand network-level discrepancies predicting PDD. These pathologically relevant neuroimaging measures
will be further combined with various non-imaging measures through a novel machine learning algorithm to
identify the comprehensive and best predictors of PDD. The tools de...

## Key facts

- **NIH application ID:** 10889167
- **Project number:** 5R01NS117547-06
- **Recipient organization:** UNIVERSITY OF ALABAMA AT BIRMINGHAM
- **Principal Investigator:** Virendra R Mishra
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $681,430
- **Award type:** 5
- **Project period:** 2020-09-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10889167

## Citation

> US National Institutes of Health, RePORTER application 10889167, Towards Generating a Multimodal and Multivariate Classification Model from Imaging and Non-Imaging Measures for Accurate Diagnosis and Monitoring of Dementia in Parkinsons disease (5R01NS117547-06). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10889167. Licensed CC0.

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